# Tokenmaxxing

*Tokenmaxxing* is a term the New York Times coined in March 2026 for a competitive culture inside AI-heavy companies where employees race to consume the most AI tokens. The metric isn't output — it's burn. Some companies maintain internal leaderboards. At least two — Shopify (Tobi Lutke) and Meta — have made AI usage a factor in performance reviews.

## Concrete benchmarks reported

- An OpenAI engineer processed 210 billion tokens in a single week.
- A Claude Code user at Anthropic runs a $150,000/month bill.
- Shopify and Meta evaluate employees on AI usage.

## Why it persists

The pattern depends on a measurement gap. Token consumption is trivially observable; the value produced by that consumption is not. So the metric you can measure substitutes for the metric you care about. Combined with the [[ai-sycophancy-loop]] — agents structurally reporting success — the operator gets a continuous signal that more spend means more progress, with no opposing signal to correct it.

The author of [[ceo-ai-psychosis]] frames this bluntly: *the leaderboard measures consumption, not output.* They recommend killing the leaderboard and running a cross-analysis on tokens-per-feature against attributable revenue, on the prediction that the result is unflattering.

## Adjacent patterns

- [[ai-great-leap-forward]] — the same substitution at the organizational level: AI usage itself becomes a KPI, divorced from whether it creates value.
- [[subprime-ai-crisis]] — the macro version: the entire revenue case for AI infrastructure depends on consumption that may not produce value.
- [[no-silver-bullet-llms]] — the inverse-correlation data: heavier AI adoption tracks with worse delivery and stability.